A Probabilistic Arsenic Exposure Assessment for Children Who Contact Chromated Copper Arsenate (CCA)‐Treated Playsets and Decks, Part 2: Sensitivity and Uncertainty Analyses

Jianping Xue, Valerie G. Zartarian, Halûk Özkaynak, Winston Dang, Graham Glen, Luther Smith and Casson Stallings

Risk Analysis, 2006, vol. 26, issue 2, 533-541

Abstract: A probabilistic model (SHEDS‐Wood) was developed to examine children's exposure and dose to chromated copper arsenate (CCA)‐treated wood, as described in Part 1 of this two‐part article. This Part 2 article discusses sensitivity and uncertainty analyses conducted to assess the key model inputs and areas of needed research for children's exposure to CCA‐treated playsets and decks. The following types of analyses were conducted: (1) sensitivity analyses using a percentile scaling approach and multiple stepwise regression; and (2) uncertainty analyses using the bootstrap and two‐stage Monte Carlo techniques. The five most important variables, based on both sensitivity and uncertainty analyses, were: wood surface residue‐to‐skin transfer efficiency; wood surface residue levels; fraction of hand surface area mouthed per mouthing event; average fraction of nonresidential outdoor time a child plays on/around CCA‐treated public playsets; and frequency of hand washing. In general, there was a factor of 8 for the 5th and 95th percentiles and a factor of 4 for the 50th percentile in the uncertainty of predicted population dose estimates due to parameter uncertainty. Data were available for most of the key model inputs identified with sensitivity and uncertainty analyses; however, there were few or no data for some key inputs. To evaluate and improve the accuracy of model results, future measurement studies should obtain longitudinal time‐activity diary information on children, spatial and temporal measurements of residue and soil concentrations on or near CCA‐treated playsets and decks, and key exposure factors. Future studies should also address other sources of uncertainty in addition to parameter uncertainty, such as scenario and model uncertainty.

Date: 2006
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https://doi.org/10.1111/j.1539-6924.2006.00748.x

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Persistent link: https://EconPapers.repec.org/RePEc:wly:riskan:v:26:y:2006:i:2:p:533-541

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